Getting to know DATA INTEGRATION TOOLS
When presented with a document, finding valuable information and coming to conclusions based on your findings sounds quite simple. However, deriving meaningful information from a document can be way harder than it sounds.
On an individual scale, reading through 2-10 documents and highlighting information is not too challenging. But multiply that number by 100 and you have a mammoth task at hand. On an organizational level, there are numerous data entries, documents, surveys, and internal communications that need to be scanned for valuable information. Add to this the challenge of the documents being in different formats and on different channels, and the task begins to look even more daunting.
With the emergence of technology that logs tasks and activity in detail, records every conversation and charts all behavior, the data available to an enterprise has expanded at a rate unforeseen and unmanageable by humans.
However, this data needs to be explored and organized. Data is collected with a singular goal in mind – to improve understanding of feedback, business performance and various facets of operation. Understanding and realization cannot occur unless the data is simplified and analyzed. In order to do this, data needs to be collected in one place, filtered, organized, and made accessible for analysis.
The process of unifying data from different platforms, feedback channels, web portals, and data management tools and collecting it in a centralized storage space as well as transforming various formats to fit into a consistent template is known as data integration. When data is organized and scraped for information, errors are corrected, problems are solved, communication becomes easier and decision making becomes effective.
Well executed data integration can have a positive effect in improving business intelligence, silo deconstruction, system-wide accessibility, big data and master data management, customer relationship management and business operation strategy.
Data Integration Tools automate the entire data integration process. They manage processes like compilation, transformation, data extraction, storage, classification, data movement, and data synchronization. They can form custom data architecture and ensure seamless flow of data in an enterprise.
Data integration can be tackled through multiple approaches, some of which are –
This approach is feasible for smaller datasets. It requires humans to manually sort and clean data, making the process time consuming and tedious.
Virtual pipelines are set up between various channels, platforms, and departments to enable 2-way point to point communication.
ETL or Extract, Transform, Load manages data compatibility digitally. Data integration tools most commonly use this methodology. In this approach, each different data format has a pre-defined transformation function that automatically conducts a sequential command list to change that particular format to another. The information is drawn out of a text, converted to a format that the enterprise already uses and then stored in a centralized data warehouse.
While the foundation of data integration tools is laid on ETL, some of the more sophisticated features of data integration tool include –
- Clipping and attribute management
- Real-time data cleaning, data migration and data synchronization
- Data consistency, data profiling, duplicate removal, and classification
- Multi-channel, multi-format data processing
- Data visualization and presentation
With workflow automation and high-volume data management capabilities, data integration tools simplify data management, enable system-wide interoperability, and improve insight generation. They also make better communication and resource sharing possible.
Many data integration tools also incorporate methods to transform data from traditional format to digital format. In collaboration with process automation tools that have embedded functions like image recognition, optical character recognition and document processing, data integration tools make it possible to convert legacy data to more modern forms. Data integration can also data management processes such as data preparation, quality management and analytics more straightforward, saving a lot of time and energy in the long run.